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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: May 27, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

AI and Neurology.

Julian Bösel^1,2,3, Rohan Mathur⁴, Lin Cheng⁴

¹Department of Neurology, University Hospital Heidelberg, Heidelberg, Germany. mail@julian-boesel.de.

Neurological Research and Practice

|February 16, 2025

Summary

Artificial Intelligence (AI) is revolutionizing neurology, enhancing diagnostics and treatment. Neurologists must understand AI's benefits and ethical considerations for safe integration into patient care.

Keywords:

AI Artificial intelligence Data-driven medicine Deep learning Machine learning Neural networks Neurology

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Area of Science:

Neurology
Artificial Intelligence
Medical Informatics

Background:

Artificial Intelligence (AI) is increasingly impacting all medical fields, including the complex discipline of neurology.
AI applications in neurology extend beyond neuroimaging to diagnostics, prognostication, prediction, decision-making, and therapy.

Purpose of the Study:

To review the fundamental principles of various AI types.
To explore the application of AI in neurology, covering diverse subspecialties.
To identify and discuss the associated risks and ethical challenges.

Main Methods:

Summarization of AI principles and their neurological applications.
Presentation of exemplary studies in acute/intensive care neurology, stroke, epilepsy, and movement disorders.
Analysis of AI's potential benefits against risks concerning ethics, safety, and equality.

Main Results:

AI demonstrates significant potential to advance neurologic diagnostics, prognostication, and treatment strategies.
Noteworthy AI applications are emerging across various neurological subspecialties.
Ethical, safety, and equality challenges must be addressed for responsible AI adoption.

Conclusions:

AI is transforming the field of neurology, necessitating prospective studies and federated learning for generalizability.
Neurologists must be adept at leveraging AI's benefits while mitigating risks related to safety, ethics, and equity.